def g10_plot_gdp_cpi_une(self, start_date, finish_date, data_type = 'cpi'):
country_group = 'g10'
if data_type == 'cpi':
df = self.get_CPI_YoY(start_date, finish_date, country_group)
elif data_type == 'gdp':
df = self.get_GDP_YoY(start_date, finish_date, country_group)
elif data_type == 'une':
df = self.get_UNE(start_date, finish_date, country_group)
df = self.hist_econ_data_factory.grasp_coded_entry(df, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'world'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'world'
gp.plotly_projection = 'Mercator'
gp.plotly_world_readable = False
gp.plotly_url = country_group + "-" + data_type
gp.title = "G10 " + data_type
gp.units = '%'
pf.plot_generic_graph(df, type = 'choropleth', adapter = 'plotly', gp = gp)
def g10_line_plot_gdp(self, start_date, finish_date):
today_root = datetime.date.today().strftime("%Y%m%d") + " "
country_group = 'g10-ez'
gdp = self.get_GDP_QoQ(start_date, finish_date, country_group)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.title = "G10 GDP"
gp.units = 'Rebased'
gp.scale_factor = Constants.plotfactory_scale_factor
gp.file_output = today_root + 'G10 UNE ' + str(
gp.scale_factor) + '.png'
gdp.columns = [x.split('-')[0] for x in gdp.columns]
gp.linewidth_2 = 3
gp.linewidth_2_series = ['United Kingdom']
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
tsc = TimeSeriesCalcs()
gdp = gdp / 100
gdp = tsc.create_mult_index_from_prices(gdp)
pf.plot_generic_graph(gdp, type='line', adapter='pythalesians', gp=gp)
def plot_strategy_signals(self, date=None, strip=None):
######## plot signals
strategy_signal = self._strategy_signal
strategy_signal = 100 * (strategy_signal)
if date is None:
last_day = strategy_signal.ix[-1].transpose().to_frame()
else:
last_day = strategy_signal.ix[date].transpose().to_frame()
if strip is not None:
last_day.index = [x.replace(strip, '') for x in last_day.index]
print(last_day)
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + " positions (% portfolio notional)"
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Positions) ' + str(
gp.scale_factor) + '.png'
pf.plot_generic_graph(last_day,
adapter='pythalesians',
type='bar',
gp=gp)
def usa_plot_une(self, start_date, finish_date):
country_group = 'usa-states'
source = 'bloomberg'
une = self.get_UNE(start_date,
finish_date,
country_group,
source='bloomberg')
une = self.hist_econ_data_factory.grasp_coded_entry(une, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'USA-states'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'usa'
gp.plotly_projection = 'albers usa'
gp.plotly_world_readable = False
gp.plotly_url = country_group + "-unemployment"
gp.title = "USA Unemployment"
gp.units = 'pc'
pf.plot_generic_graph(une, type='choropleth', adapter='plotly', gp=gp)
def g10_plot_gdp_cpi_une(self, start_date, finish_date, data_type='cpi'):
country_group = 'g10'
if data_type == 'cpi':
df = self.get_CPI_YoY(start_date, finish_date, country_group)
elif data_type == 'gdp':
df = self.get_GDP_QoQ(start_date, finish_date, country_group)
elif data_type == 'une':
df = self.get_UNE(start_date, finish_date, country_group)
df = self.hist_econ_data_factory.grasp_coded_entry(df, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'world'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'world'
gp.plotly_projection = 'Mercator'
gp.plotly_world_readable = False
gp.plotly_url = country_group + "-" + data_type
gp.title = "G10 " + data_type
gp.units = '%'
pf.plot_generic_graph(df, type='choropleth', adapter='plotly', gp=gp)
def g10_line_plot_cpi(self, start_date, finish_date):
today_root = datetime.date.today().strftime("%Y%m%d") + " "
country_group = 'g10-ez'
cpi = self.get_CPI_YoY(start_date, finish_date, country_group)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.title = "G10 CPI YoY"
gp.units = '%'
gp.scale_factor = 3
gp.file_output = today_root + 'G10 CPI YoY ' + str(gp.scale_factor) + '.png'
cpi.columns = [x.split('-')[0] for x in cpi.columns]
gp.linewidth_2 = 3
gp.linewidth_2_series = ['United States']
pf.plot_generic_graph(cpi, type = 'line', adapter = 'pythalesians', gp = gp)
def g10_line_plot_une(self, start_date, finish_date):
today_root = datetime.date.today().strftime("%Y%m%d") + " "
country_group = 'g10-ez'
une = self.get_UNE(start_date, finish_date, country_group)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.title = "G10 Unemployment Rate (%)"
gp.units = '%'
gp.scale_factor = Constants.plotfactory_scale_factor
gp.file_output = today_root + 'G10 UNE ' + str(gp.scale_factor) + '.png'
une.columns = [x.split('-')[0] for x in une.columns]
gp.linewidth_2 = 3
gp.linewidth_2_series = ['United States']
pf.plot_generic_graph(une, type = 'line', adapter = 'pythalesians', gp = gp)
def g10_line_plot_une(self, start_date, finish_date):
today_root = datetime.date.today().strftime("%Y%m%d") + " "
country_group = 'g10-ez'
une = self.get_UNE(start_date, finish_date, country_group)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.title = "G10 Unemployment Rate (%)"
gp.units = '%'
gp.scale_factor = Constants.plotfactory_scale_factor
gp.file_output = today_root + 'G10 UNE ' + str(
gp.scale_factor) + '.png'
une.columns = [x.split('-')[0] for x in une.columns]
gp.linewidth_2 = 3
gp.linewidth_2_series = ['United States']
pf.plot_generic_graph(une, type='line', adapter='pythalesians', gp=gp)
def g10_line_plot_cpi(self, start_date, finish_date):
today_root = datetime.date.today().strftime("%Y%m%d") + " "
country_group = 'g10-ez'
cpi = self.get_CPI_YoY(start_date, finish_date, country_group)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.title = "G10 CPI YoY"
gp.units = '%'
gp.scale_factor = 3
gp.file_output = today_root + 'G10 CPI YoY ' + str(
gp.scale_factor) + '.png'
cpi.columns = [x.split('-')[0] for x in cpi.columns]
gp.linewidth_2 = 3
gp.linewidth_2_series = ['United States']
pf.plot_generic_graph(cpi, type='line', adapter='pythalesians', gp=gp)
def europe_plot_une(self, start_date, finish_date):
country_group = 'all-europe'
une = self.get_UNE(start_date, finish_date, country_group)
une = self.hist_econ_data_factory.grasp_coded_entry(une, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'europe'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'europe'
gp.plotly_projection = 'Mercator'
gp.plotly_world_readable = False
gp.plotly_url = country_group + "-unemployment"; gp.title = "Europe Unemployment"
gp.units = '%'
pf.plot_generic_graph(une, type = 'choropleth', adapter = 'plotly', gp = gp)
def usa_plot_une(self, start_date, finish_date):
country_group = 'usa-states'; source = 'bloomberg'
une = self.get_UNE(start_date, finish_date, country_group, source = 'bloomberg')
une = self.hist_econ_data_factory.grasp_coded_entry(une, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'USA-states'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'usa'
gp.plotly_projection = 'albers usa'
gp.plotly_world_readable = False
gp.plotly_url = country_group + "-unemployment"
gp.title = "USA Unemployment"
gp.units = 'pc'
pf.plot_generic_graph(une, type = 'choropleth', adapter = 'plotly', gp = gp)
def plot_strategy_signals(self, date = None, strip = None):
######## plot signals
strategy_signal = self._strategy_signal
strategy_signal = 100 * (strategy_signal)
if date is None:
last_day = strategy_signal.ix[-1].transpose().to_frame()
else:
last_day = strategy_signal.ix[date].transpose().to_frame()
if strip is not None:
last_day.index = [x.replace(strip, '') for x in last_day.index]
print(last_day)
pf = PlotFactory()
gp = GraphProperties()
gp.title = self.FINAL_STRATEGY + " positions (% portfolio notional)"
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Positions) ' + str(gp.scale_factor) + '.png'
pf.plot_generic_graph(last_day, adapter = 'pythalesians', type = 'bar', gp = gp)
def world_plot_cpi(self, start_date, finish_date):
country_group = 'world-liquid'
cpi = self.get_CPI_YoY(start_date, finish_date, country_group)
cpi = self.hist_econ_data_factory.grasp_coded_entry(cpi, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'world'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'world'
gp.plotly_projection = 'Mercator'
gp.plotly_world_readable = False
gp.plotly_url = str(country_group) + "-cpi"
gp.title = "World Liquid CPI YoY"
gp.units = '%'
pf.plot_generic_graph(cpi, type='choropleth', adapter='plotly', gp=gp)
def europe_plot_une(self, start_date, finish_date):
country_group = 'all-europe'
une = self.get_UNE(start_date, finish_date, country_group)
une = self.hist_econ_data_factory.grasp_coded_entry(une, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'europe'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'europe'
gp.plotly_projection = 'Mercator'
gp.plotly_world_readable = False
gp.plotly_url = country_group + "-unemployment"
gp.title = "Europe Unemployment"
gp.units = '%'
pf.plot_generic_graph(une, type='choropleth', adapter='plotly', gp=gp)
def g10_line_plot_gdp(self, start_date, finish_date):
today_root = datetime.date.today().strftime("%Y%m%d") + " "
country_group = 'g10-ez'
gdp = self.get_GDP_QoQ(start_date, finish_date, country_group)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.title = "G10 GDP"
gp.units = 'Rebased'
gp.scale_factor = Constants.plotfactory_scale_factor
gp.file_output = today_root + 'G10 UNE ' + str(gp.scale_factor) + '.png'
gdp.columns = [x.split('-')[0] for x in gdp.columns]
gp.linewidth_2 = 3
gp.linewidth_2_series = ['United Kingdom']
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
tsc = TimeSeriesCalcs()
gdp = gdp / 100
gdp = tsc.create_mult_index_from_prices(gdp)
pf.plot_generic_graph(gdp, type = 'line', adapter = 'pythalesians', gp = gp)
def world_plot_cpi(self, start_date, finish_date):
country_group = 'world-liquid'
cpi = self.get_CPI_YoY(start_date, finish_date, country_group)
cpi = self.hist_econ_data_factory.grasp_coded_entry(cpi, -1)
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'world'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'world'
gp.plotly_projection = 'Mercator'
gp.plotly_world_readable = False
gp.plotly_url = str(country_group) + "-cpi"
gp.title = "World Liquid CPI YoY"
gp.units = '%'
pf.plot_generic_graph(cpi, type = 'choropleth', adapter = 'plotly', gp = gp)
df = tsc.create_mult_index_from_prices(
ltsf.harvest_time_series(time_series_request))
gp = GraphProperties()
gp.title = "S&P500 vs Apple"
# plot first with PyThalesians and then Plotly (via Cufflinks)
# just needs 1 word to change
# (although, note that AdapterCufflinks does have some extra parameters that can be set in
# GraphProperties)
gp.plotly_username = '******'
gp.plotly_world_readable = True
pf = PlotFactory()
pf.plot_generic_graph(df, type='line', adapter='pythalesians', gp=gp)
pf.plot_generic_graph(df, type='line', adapter='cufflinks', gp=gp)
# test simple Plotly bar charts - average differences in EURUSDV1M-1Y vol and USDJPYV1M-1Y slope over past sixth months
if True:
from datetime import timedelta
ltsf = LightTimeSeriesFactory()
end = datetime.datetime.utcnow()
start = end - timedelta(days=180)
tickers = ['EURUSDV1M', 'EURUSDV1Y', 'USDJPYV1M', 'USDJPYV1Y']
vendor_tickers = [
'EURUSDV1M BGN Curncy', 'EURUSDV1Y BGN Curncy', 'USDJPYV1M BGN Curncy',
'USDJPYV1Y BGN Curncy'
]
ltsf = LightTimeSeriesFactory()
tsc = TimeSeriesCalcs()
df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request))
gp = GraphProperties()
gp.html_file_output = "output_data/apple.htm"
gp.title = "S&P500 vs Apple"
# plot first with PyThalesians and then Bokeh
# just needs 1 word to change
gp.display_legend = False
pf = PlotFactory()
pf.plot_generic_graph(df, type = 'line', adapter = 'pythalesians', gp = gp)
pf.plot_generic_graph(df, type = 'line', adapter = 'bokeh', gp = gp)
# test simple Bokeh bar charts - monthly returns over past 6 months
if True:
from datetime import timedelta
ltsf = LightTimeSeriesFactory()
end = datetime.datetime.utcnow()
start = end - timedelta(days=180)
tickers = ['S&P500', 'FTSE', 'Nikkei']
vendor_tickers = ['SPX Index', 'UKX Index', 'NKY Index']
time_series_request = TimeSeriesRequest(
start_date = start, # start date
cache_algo='internet_load_return') # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
# plot data
gp = GraphProperties()
pf = PlotFactory()
gp.title = 'Spot values'
gp.file_output = 'output_data/demo.png'
gp.scale_factor = 2
gp.style_sheet = 'pythalesians'
# first use PyThalesians matplotlib wrapper
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
pf.plot_generic_graph(daily_vals, gp=gp, type='line')
# use modified 538 Matplotlib stylesheet
gp.style_sheet = '538-pythalesians'
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
# use miletus matplotlib stylesheet
gp.style_sheet = 'miletus-pythalesians'
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
# use ggplot matplotlib styleheet
gp.scale_factor = 1
gp.display_brand_label = False
gp.display_source = False
gp.style_sheet = 'ggplot-pythalesians'
gp.display_mpld3 = True
ltsf = LightTimeSeriesFactory()
tsc = TimeSeriesCalcs()
df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request))
gp = GraphProperties()
gp.html_file_output = "output_data/apple.htm"
gp.title = "S&P500 vs Apple"
# plot first with PyThalesians and then Bokeh
# just needs 1 word to change
gp.display_legend = False
pf = PlotFactory()
pf.plot_generic_graph(df, type="line", adapter="pythalesians", gp=gp)
pf.plot_generic_graph(df, type="line", adapter="bokeh", gp=gp)
# test simple Bokeh bar charts - monthly returns over past 6 months
if True:
from datetime import timedelta
ltsf = LightTimeSeriesFactory()
end = datetime.datetime.utcnow()
start = end - timedelta(days=180)
tickers = ["S&P500", "FTSE", "Nikkei"]
vendor_tickers = ["SPX Index", "UKX Index", "NKY Index"]
time_series_request = TimeSeriesRequest(
cache_algo = 'internet_load_return') # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
# plot data
gp = GraphProperties()
pf = PlotFactory()
gp.title = 'Spot values'
gp.file_output = 'demo.png'
gp.scale_factor = 2
gp.style_sheet = 'pythalesians'
# first use PyThalesians matplotlib wrapper
pf.plot_line_graph(daily_vals, adapter = 'pythalesians', gp = gp)
pf.plot_generic_graph(daily_vals, gp = gp, type = 'line')
# use modified 538 Matplotlib stylesheet
gp.style_sheet = '538-pythalesians'
pf.plot_line_graph(daily_vals, adapter = 'pythalesians', gp = gp)
# use miletus matplotlib stylesheet
gp.style_sheet = 'miletus-pythalesians'
pf.plot_line_graph(daily_vals, adapter = 'pythalesians', gp = gp)
# use ggplot matplotlib styleheet
gp.scale_factor = 1
gp.display_brand_label = False
gp.display_source = False
gp.style_sheet = 'ggplot-pythalesians'
gp.display_mpld3 = True
from pythalesians.graphics.graphs.graphproperties import GraphProperties
gp = GraphProperties()
pf = PlotFactory()
gp.plotly_location_mode = 'USA-states'
gp.plotly_choropleth_field = 'Val'
gp.plotly_scope = 'usa'
gp.plotly_projection = 'albers usa'
gp.plotly_url = country_group + data_type.replace(' ', '-')
gp.plotly_world_readable = False
gp.title = title
gp.units = 'Value'
# do a map plot by US state
pf.plot_generic_graph(df, type = 'choropleth', adapter = 'cufflinks', gp = gp)
#### uses CommonEconDataFactory to get more common forms of economic data and plot
####
if True:
logger = LoggerManager.getLogger(__name__)
cedf = CommonEconDataFactory()
start_date = '01 Jan 2014'
finish_date = datetime.datetime.utcnow()
# select as appropriate!
# cedf.g10_plot_gdp_cpi_une(start_date, finish_date)
# cedf.usa_plot_une(start_date, finish_date)
# cedf.world_plot_une(start_date, finish_date)
# cedf.world_plot_cpi(start_date, finish_date)
) # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
# plot data
gp = GraphProperties()
pf = PlotFactory()
gp.title = "Spot values"
gp.file_output = "output_data/demo.png"
gp.scale_factor = 2
gp.style_sheet = "pythalesians"
# first use PyThalesians matplotlib wrapper
pf.plot_line_graph(daily_vals, adapter="pythalesians", gp=gp)
pf.plot_generic_graph(daily_vals, gp=gp, type="line")
# use modified 538 Matplotlib stylesheet
gp.style_sheet = "538-pythalesians"
pf.plot_line_graph(daily_vals, adapter="pythalesians", gp=gp)
# use miletus matplotlib stylesheet
gp.style_sheet = "miletus-pythalesians"
pf.plot_line_graph(daily_vals, adapter="pythalesians", gp=gp)
# use ggplot matplotlib styleheet
gp.scale_factor = 1
gp.display_brand_label = False
gp.display_source = False
gp.style_sheet = "ggplot-pythalesians"
gp.display_mpld3 = True
